跨房间被动WiFi跟踪：目标轨迹与虚拟发射器位置的联合估计
Cross-Room Passive WiFi Tracking: Joint Estimation of Target Trajectory and Virtual Transmitter Location
- University of Technology Sydney（悉尼科技大学）
- Chalmers University of Technology（查尔姆斯理工大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对跨房间NLOS下Tx未知的被动WiFi跟踪难题，CoTrack通过虚拟Tx与轨迹联合估计及多起点优化实现自校准，中位轨迹误差1.14米。
AI中文摘要：
室内双基地WiFi感知通常需要在跨房间场景下工作，此时墙壁会阻挡发射器（Tx）与接收器（Rx）之间的直接视距（LOS）路径。从信道状态信息（CSI）导出的时延、到达角（AoA）和多普勒估计中恢复人体轨迹是困难的，因为Tx位置通常未知，且非视距（NLOS）传播与直接路径几何不一致。本文提出CoTrack，一种无需Tx位置的自校准被动WiFi跟踪方案。CoTrack提取一个主要的人体感应响应，并通过虚拟Tx表示其发射端传播，该虚拟Tx与人体轨迹联合估计。由于相似的测量结果可能由不同的虚拟Tx-轨迹对解释，CoTrack采用多起点非线性最小二乘法，仅当最佳拟合候选集中在公共虚拟Tx位置附近时才接受初始化。随后应用在线交替优化进行连续跟踪。我们分析了联合估计问题的局部可辨识性，并推导了相应的Cramér-Rao下界。在六个LOS和跨房间NLOS实验中，CoTrack实现了1.14米的中位轨迹误差（第80百分位：1.42米）和0.47米的中位虚拟Tx偏差（第80百分位：0.54米）。
英文摘要：
Indoor bistatic WiFi sensing must often operate across rooms, where intervening walls block the direct line-of-sight (LOS) path between the transmitter (Tx) and receiver (Rx). Recovering a human trajectory from channel-state-information (CSI)-derived delay, angle-of-arrival (AoA), and Doppler estimates is difficult because the Tx position is often unknown and NLOS propagation is inconsistent with direct-path geometry. This paper presents CoTrack, a self-calibrating passive WiFi tracking scheme that does not require the Tx position. CoTrack extracts one dominant human-induced response and represents its transmitter-side propagation by a virtual Tx, which it estimates jointly with the human trajectory. Because similar measurements can be explained by different virtual-Tx--trajectory pairs, CoTrack uses multi-start nonlinear least squares and accepts an initialization only when the best-fitting candidates concentrate around a common virtual-Tx position. It then applies online alternating optimization for continuous tracking. We analyze the local identifiability of the joint estimation problem and derive the corresponding Cramér--Rao bound. Across six LOS and cross-room NLOS experiments, CoTrack achieves a median trajectory error of 1.14m(80th percentile: 1.42m) and a median virtual-Tx discrepancy of 0.47m(80th percentile: 0.54m).
